Analysis of the domain of applicability of an algorithm for a resources system selection problem for Distributed/Agile/Virtual Enterprises integration

The process of resources systems selection takes an important part in Distributed/Agile/Virtual Enterprises (D/A/V Es) integration. However, the resources systems selection is still a difficult matter to solve in a D/A/VE, as it is pointed out in this paper. Globally, we can say that the selection problem has been equated from different aspects, originating different kinds of models/algorithms to solve it. In order to assist the development of a web prototype tool (broker tool), intelligent and flexible, that integrates all the selection model activities and tools, and with the capacity to adequate to each D/A/V E project or instance (this is the major goal of our final project), we intend in this paper to show: a formulation of a kind of resources selection problem and the limitations of the algorithms proposed to solve it. We formulate a particular case of the problem as an integer programming, which is solved using simplex and branch and bound algorithms, and identify their performance limitations (in terms of processing time) based on simulation results. These limitations depend on the number of processing tasks and on the number of pre-selected resources per processing tasks, defining the domain of applicability of the algorithms for the problem studied. The limitations detected open the necessity of the application of other kind of algorithms (approximate solution algorithms) outside the domain of applicability founded for the algorithms simulated. However, for a broker tool it is very important the knowledge of algorithms limitations, in order to, based on problem features, develop and select the most suitable algorithm that guarantees a good performance.

Development of biocompatible and “smart” porous structures using CO2-assisted processes

Dissertação apresentada para a obtenção do grau de Doutor em Engenharia Química, especialidade Engenharia da Reacção Química, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Dinuclear Mn (II,II) complexes: magnetic properties and microwave assisted oxidation of alcohols

A series of six new mixed-ligand dinuclear Mn(II, II) complexes of three different hydrazone Schiff bases (H3L1, H3L2 and H3L3), derived from condensation of the aromatic acid hydrazides benzohydrazide, 2-aminobenzohydrazide or 2-hydroxybenzohydrazide, with 2,3-dihydroxy benzaldehyde, respectively, is reported. Reactions of Mn(NO3)(2) center dot 4H(2)O with the H3L1-3 compounds, in the presence of pyridine (1 : 1 : 1 mole ratio), in methanol at room temperature, yield [Mn(H2L1)(py)(H2O)](2)(NO3)(2) center dot 2H(2)O (1 center dot 2H(2)O), [Mn(H2L2)(py)(CH3OH)](2)(NO3)(2) center dot 4H(2)O (2 center dot 4H(2)O) and [Mn(H2L3)(py)(H2O)](2)(NO3)(2) (3) respectively, whereas the use of excess pyridine yields complexes with two axially coordinated pyridine molecules at each Mn(II) centre, viz. [Mn(H2L1)(py)(2)] 2(NO3)(2) center dot H2O (4 center dot H2O), [Mn(H2L2)(py) H-O (6 center dot 2CH(3)OH), respectively. In all the complexes, the (H2L1-3)-ligand coordinates in the keto form. Complexes 1 center dot 2H(2)O, 2 center dot 4H(2)O, 4 center dot H2O, 5 center dot 2H(2)O and 6 center dot 2CH(3)OH are characterized by single crystal X-ray diffraction analysis. The complexes 1, 2 and 6, having different coordination environments, have been selected for variable temperature magnetic susceptibility measurements to examine the nature of magnetic interaction between magnetically coupled Mn(II) centres and also for exploration of the catalytic activity towards microwave assisted oxidation of alcohols. A yield of 81% (acetophenone) is obtained using a maximum of 0.4% molar ratio of catalyst relative to the substrate in the presence of TEMPO and in aqueous basic solution, under mild conditions.

Optimal location of the workpiece in a PKM-based machining robotic cell

Most machining tasks require high accuracy and are carried out by dedicated machine-tools. On the other hand, traditional robots are flexible and easy to program, but they are rather inaccurate for certain tasks. Parallel kinematic robots could combine the accuracy and flexibility that are usually needed in machining operations. Achieving this goal requires proper design of the parallel robot. In this chapter, a multi-objective particle swarm optimization algorithm is used to optimize the structure of a parallel robot according to specific criteria. Afterwards, for a chosen optimal structure, the best location of the workpiece with respect to the robot, in a machining robotic cell, is analyzed based on the power consumed by the manipulator during the machining process.

A genetic algorithm for project scheduling with multi-modes and renewable resources

This paper presents a genetic algorithm-based approach for project scheduling with multi-modes and renewable resources. In this problem activities of the project may be executed in more than one operating mode and renewable resource constraints are imposed. The objective function is the minimization of the project completion time. The idea of this approach is integrating a genetic algorithm with a schedule generation scheme. This study also proposes applying a local search procedure trying to yield a better solution when the genetic algorithm and the schedule generation scheme obtain a solution. The experimental results show that this algorithm is an effective method for solving this problem.

Microwave-assisted and solvent-free peroxidative oxidation of 1-phenylethanol to acetophenone with a CuII-TEMPO catalytic system

The water-soluble copper(II) complex [Cu(H2R)(HL)]center dot H2O (1) was prepared by reaction of copper(II) nitrate hydrate with (E)-2-(((1-hydroxynaphthalen-2-yl)methylene)amino) benzenesulfonic acid (H2L) and diethanolamine (H3R). It was characterized by IR and ESI-MS spectroscopies, elemental and X-ray crystal structural analyses. 1 shows a high catalytic activity for the solvent-free microwave (MW) assisted oxidation of 1-phenylethanol with tert-butylhydroperoxide, leading, in the presence of TEMPO, to yields up to 85% (TON = 850) in a remarkably short reaction time (15 min, with the corresponding TOE value of 3.40 x 10(3) h(-1)) under low power (25W) MW irradiation. Crown Copyright (C) 2014 Published by Elsevier B.V. All rights reserved.

Project scheduling under multiple resources constraints using a genetic algorithm

The resource constrained project scheduling problem (RCPSP) is a difficult problem in combinatorial optimization for which extensive investigation has been devoted to the development of efficient algorithms. During the last couple of years many heuristic procedures have been developed for this problem, but still these procedures often fail in finding near-optimal solutions. This paper proposes a genetic algorithm for the resource constrained project scheduling problem. The chromosome representation of the problem is based on random keys. The schedule is constructed using a heuristic priority rule in which the priorities and delay times of the activities are defined by the genetic algorithm. The approach was tested on a set of standard problems taken from the literature and compared with other approaches. The computational results validate the effectiveness of the proposed algorithm.

Project scheduling using a competitive genetic algorithm

- The resource constrained project scheduling problem (RCPSP) is a difficult problem in combinatorial optimization for which extensive investigation has been devoted to the development of efficient algorithms. During the last couple of years many heuristic procedures have been developed for this problem, but still these procedures often fail in finding near-optimal solutions. This paper proposes a genetic algorithm for the resource constrained project scheduling problem. The chromosome representation of the problem is based on random keys. The schedule is constructed using a heuristic priority rule in which the priorities and delay times of the activities are defined by the genetic algorithm. The approach was tested on a set of standard problems taken from the literature and compared with other approaches. The computational results validate the effectiveness of the proposed algorithm

InOx thin films deposited by plasma assisted evaporation: application in light shutters

An integration of undoped InOx and commercial ITO thin films into laboratory assembled light shutter devices is made. Accordingly, undoped transparent conductive InOx thin films, about 100 nm thick, are deposited by radiofrequency plasma enhanced reactive thermal evaporation (rf-PERTE) of indium teardrops with no intentional heating of the glass substrates. The process of deposition occurs at very low deposition rates (0.1-0.3 nm/s) to establish an optimized reaction between the oxygen plasma and the metal vapor. These films show the following main characteristics: transparency of 87% (wavelength, lambda = 632.8 nm) and sheet resistance of 52 Omega/sq; while on commercial ITO films the transparency was of 92% and sheet resistance of 83 Omega/sq. The InOx thin film surface characterized by AFM shows a uniform grain texture with a root mean square surface roughness of Rq similar to 2.276 nm. In contrast, commercial ITO topography is characterized by two regions: one smoother with Rq similar to 0.973 nm and one with big grains (Rq similar to 3.617 nm). For the shutters assembled using commercial ITO, the light transmission coefficient (Tr) reaches the highest value (Tr-max) of 89% and the lowest (Tr-min) of 1.3% [13], while for the InOx shutters these values are 80.1% and 3.2%, respectively. Regarding the electric field required to achieve 90% of the maximum transmission in the ON state (E-on), the one presented by the devices assembled with commercial ITO coated glasses is 2.41 V/mu m while the one presented by the devices assembled with InOx coated glasses is smaller, 1.77 V/mu m. These results corroborate the device quality that depends on the base materials and fabrication process used. (C) 2014 Elsevier Ltd. All rights reserved.

A biased random-key genetic algorithm with forward-backward improvement for the resource constrained project scheduling problem

This paper presents a biased random-key genetic algorithm for the resource constrained project scheduling problem. The chromosome representation of the problem is based on random keys. Active schedules are constructed using a priority-rule heuristic in which the priorities of the activities are defined by the genetic algorithm. A forward-backward improvement procedure is applied to all solutions. The chromosomes supplied by the genetic algorithm are adjusted to reflect the solutions obtained by the improvement procedure. The heuristic is tested on a set of standard problems taken from the literature and compared with other approaches. The computational results validate the effectiveness of the proposed algorithm.

A genetic algorithm for the job shop scheduling with a new local search using Monte Carlo method

This paper presents a methodology for applying scheduling algorithms using Monte Carlo simulation. The methodology is based on a decision support system (DSS). The proposed methodology combines a genetic algorithm with a new local search using Monte Carlo Method. The methodology is applied to the job shop scheduling problem (JSSP). The JSSP is a difficult problem in combinatorial optimization for which extensive investigation has been devoted to the development of efficient algorithms. The methodology is tested on a set of standard instances taken from the literature and compared with others. The computation results validate the effectiveness of the proposed methodology. The DSS developed can be utilized in a common industrial or construction environment.

Human-robot teamwork: a knowledge-based solution

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Electrotécnica e de Computadores

A two-level genetic algorithm for the multi-mode resource-constrained project scheduling problem

This paper presents a genetic algorithm for the multimode resource-constrained project scheduling problem (MRCPSP), in which multiple execution modes are available for each of the activities of the project. The objective function is the minimization of the construction project completion time. To solve the problem, is applied a two-level genetic algorithm, which makes use of two separate levels and extend the parameterized schedule generation scheme by introducing an improvement procedure. It is evaluated the quality of the schedule and present detailed comparative computational results for the MRCPSP, which reveal that this approach is a competitive algorithm.

A mobile robot platform for open learning based on serious games and remote laboratories

Within the pedagogical community, Serious Games have arisen as a viable alternative to traditional course-based learning materials. Until now, they have been based strictly on software solutions. Meanwhile, research into Remote Laboratories has shown that they are a viable, low-cost solution for experimentation in an engineering context, providing uninterrupted access, low-maintenance requirements, and a heightened sense of reality when compared to simulations. This paper will propose a solution where both approaches are combined to deliver a Remote Laboratory-based Serious Game for use in engineering and school education. The platform for this system is the WebLab-Deusto Framework, already well-tested within the remote laboratory context, and based on open standards. The laboratory allows users to control a mobile robot in a labyrinth environment and take part in an interactive game where they must locate and correctly answer several questions, the subject of which can be adapted to educators' needs. It also integrates the Google Blockly graphical programming language, allowing students to learn basic programming and logic principles without needing to understand complex syntax.

Implementation of fractional-order electromagnetic potential through a genetic algorithm

Several phenomena present in electrical systems motivated the development of comprehensive models based on the theory of fractional calculus (FC). Bearing these ideas in mind, in this work are applied the FC concepts to define, and to evaluate, the electrical potential of fractional order, based in a genetic algorithm optimization scheme. The feasibility and the convergence of the proposed method are evaluated.